9 citations · 9 across the 11 of their papers we have counts for
5 papers · 1 filter
Data-free neural PDE solvers based on Graph Neural Networks and weak forms
Mikel M. Iparraguirre, Iciar Alfaro, David Gonzalez +1
We present a physics-informed, data-free neural solver for partial differential equations, built on a graph neural network architecture that utilises message passing. By relying on…
A Graph Neural Network approach to zero-shot Digital Twins
Alicia Tierz, Icíar Alfaro, David González +1
Traditional Predictive Digital Twins often remain geometrically rigid, requiring extensive retraining or fine-tuning whenever the underlying physical domain or boundary conditions…
Physics-informed, Generative Adversarial Design of Funicular Shells
Rúben Lourenço, Icíar Alfaro, Beatriz Moya +1
Shell structures are pivotal in the fields of architecture and engineering, due to their aesthetic appeal and structural efficiency. Recently, 3D concrete printing has reignited th…
Variational Graph Neural Networks for Uncertainty Quantification in Inverse Problems
David Gonzalez, Alba Muixi, Beatriz Moya +1
The increasingly wide use of deep machine learning techniques in computational mechanics has significantly accelerated simulations of problems that were considered unapproachable j…
MeshGraphNet-Transformer: Scalable Mesh-based Learned Simulation for Solid Mechanics
Mikel M. Iparraguirre, Iciar Alfaro, David Gonzalez +1
We present MeshGraphNet-Transformer (MGN-T), a novel architecture that combines the global modeling capabilities of Transformers with the geometric inductive bias of MeshGraphNets,…